Data plotten/visualiseren

In de video's werken we met de bergregio van de drankgegevens. In deze oefeningen ga je aan de slag met de grootstedelijke gebieden van de staat om hun producten te voorspellen.

Er zijn drie producten in de grootstedelijke gebieden: high-end, low-end en speciaal. Het speciale product wordt nergens anders in de staat verkocht. De kolomnamen voor de verkoop van deze drie producten zijn respectievelijk MET.hi, MET.lo en MET.sp. Voordat je elk van deze producten apart bekijkt, gaan we eerst de totale verkoop in de grootstedelijke regio plotten. Het object bev_xts is al voor je geladen in je workspace.

Deze oefening maakt deel uit van de cursus

Vraag naar producten voorspellen in R

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Oefeninstructies

Maak drie objecten met de namen MET_hi, MET_lo en MET_sp op basis van de drie kolommen MET.hi, MET.lo en MET.sp uit je xts-object bev_xts.
Tel de verkoop van deze drie reeksen bij elkaar op in één object genaamd MET_t.
Plot het object MET_t om de verkoop in de grootstedelijke regio van de staat te visualiseren.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Create the individual region sales as their own objects
MET_hi <- bev_xts[,"MET.hi"]
MET_lo <- bev_xts[,"___"]
MET_sp <- ___[,"___"]

# Sum the region sales together
MET_t <- ___ + ___ + ___

# Plot the metropolitan region total sales
plot(MET_t)

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

Vraag naar producten voorspellen in R

SkillTag.level.intermediateSkillTag.label

4.8+

Begin de cursus gratis

When it comes to forecasting, time series modeling is a great place to start! You need to forecast out the future values of sales demand and a good baseline approach would be ARIMA models. In this chapter you'll learn how to quickly implement ARIMA models and get good initial forecasts for future product demand.

Exercise 1: Data laden in een xts-object Exercise 2: Gegevens importeren Exercise 3: Data plotten/visualiseren

Huidige oefening

Exercise 4: ARIMA-tijdreeks 101 Exercise 5: auto.arima()-functie Exercise 6: auto.arima interpreteren Exercise 7: Voorspellen en Evalueren Exercise 8: Forecast-functie Exercise 9: MAPE en MAE berekenen Exercise 10: Forecast visualiseren Exercise 11: Betrouwbaarheidsintervallen voor de voorspelling

Economic theory has a lot to say about predicting values of demand. Obviously, external factors like price, seasonality, and timing of promotions will drive some aspects of product demand. In this chapter you'll learn about the basics around price elasticity models and how to incorporate seasonality and promotion timing factors into our product demand forecasts.

Exercise 1: Price elasticity Exercise 2: Calculating price elasticity Exercise 3: Interpret results from elasticity Exercise 4: Seasonal / holiday / promotional effects Exercise 5: Visualize holiday / promotion effects Exercise 6: Create holiday / promotional effect variables Exercise 7: Regression for holiday / promotional effects Exercise 8: Significant holiday or promotional effects?Exercise 9: Forecasting with regression Exercise 10: Create future predictor variables Exercise 11: Forecast future values of demand Exercise 12: Visualizing forecasts of regression

Time series models and pricing regressions don't have to be thought of as separate approaches to product demand forecasting. They can be combined! In this chapter you'll learn about two ways of "combining" the information gained in both modeling approaches - transfer functions and forecast ensembling.

Exercise 1: Residuals from regression model Exercise 2: Calculating residuals from regression Exercise 3: Visualizing residuals Exercise 4: Forecasting residuals Exercise 5: auto.arima function Exercise 6: Forecasting residuals with time series Exercise 7: Visualizing residual forecasts Exercise 8: Transfer functions & ensembling Exercise 9: Combining residuals from regression & time series Exercise 10: Visualizing transfer function forecast Exercise 11: Calculating transfer function MAPE and MAE Exercise 12: ARIMA Forecasting Exercise 13: Ensembling of Forecasts

Everything up until this point deals with making individual models for forecasting product demand. However, we haven't taken advantage of the fact that all of these products form a product hierarchy of sales. Products make up regions and regions make up states. How can we ensure that our forecasts reconcile correctly up and down the hierarchy? In this chapter you'll learn about hierarchical forecasting and how to use it to your advantage in forecasting product demand.

Exercise 1: Bottom-Up Hierarchical Forecasting Exercise 2: Build time series forecast for new product Exercise 3: Build regression forecast for new product Exercise 4: Ensemble forecast for new product Exercise 5: Calculate bottom-up forecast for whole region Exercise 6: Top-Down Hierarchical Forecasting Exercise 7: Build time series forecast at regional level Exercise 8: Using average historical proportions Exercise 9: Using proportion of historical averages Exercise 10: Middle-Out Hierarchical Forecasting Exercise 11: Build time series forecast for new region Exercise 12: Top-down forecast for new region Exercise 13: Bottom-up forecast for whole state Exercise 14: Congratulations!